论文标题
IEEE 802.11AX的TWT机制的联合优化
Joint optimization of TWT mechanism and scheduling for IEEE 802.11ax
论文作者
论文摘要
IEEE 802.11AX作为最新的无线局域网（WLANS）标准，在人口稠密的区域中，网络吞吐量，覆盖范围和能源效率可取得巨大改进。与以前的IEEE 802.11标准不同的标准能力节省机制具有有限的功能和灵活性不同，802.11AX具有一种称为目标唤醒时间（TWT）的不同机制，在每个TWT间隔和不同的STA之后，站点（Stas）仅在不同时间唤醒后才醒来，这取决于其应用程序要求。例如，对于在物联网应用程序中发生的周期性数据到达，STA可以通过遵循数据周期来唤醒，然后进入睡眠模式更长的时间，而流量较高的STA可以具有较短的TWT间隔，以唤醒更多的频率。此外，随着多用户传输能力添加到802.11ax中，多个Stas可以具有相同的TWT间隔并同时醒来，因此有一个很好的机会，可以通过在TWT间隔中安排多个Stas来减少能源消耗并增加网络吞吐量，从而获得无碰撞传输的机会。在本文中，我们一起研究了Stas调度和TWT间隔分配的问题，以减少网络的整体能源消耗。我们提出了一种动态选择的算法，鉴于其流量和渠道条件，将其分配为最合适的TWT间隔。我们通过Lyapunov优化框架分析了算法，并表明我们的算法以增加队列大小的价格任意接近最佳性能。模拟结果表明，与随机运行的TWT分配的基准算法相比，我们的算法消耗较少的功率，并支持更高的流量。
IEEE 802.11ax as the newest Wireless Local Area Networks (WLANS) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called Target Wake Time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go to sleep mode for a much longer time, and STAs with high traffic volume can have shorter TWT interval to wake up more frequency. Moreover, as multi-user transmission capability is added to 802.11ax, multiple STAs can have the same TWT interval and wake up at the same time, and hence there is a great opportunity to have collision-free transmission by scheduling multiple STAs on appreciate TWT intervals to reduce energy consumption and also increase network throughput. In this paper, we investigate the problem of STAs scheduling and TWT interval assignment together to reduce overall energy consumption of the network. We propose an algorithm that dynamically selects STAs to be served and assigns them the most suitable TWT interval given their traffic and channel conditions. We analyze our algorithm through Lyapunov optimization framework and show that our algorithm is arbitrarily close to the optimal performance at the price of increased queue sizes. Simulation results show that our algorithm consumes less power and support higher traffic compared to a benchmark algorithm that operates randomly for TWT assignment.
